Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81647
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dc.contributorDepartment of Building and Real Estate-
dc.creatorHan, S-
dc.creatorLi, H-
dc.creatorLi, MC-
dc.creatorRose, T-
dc.date.accessioned2020-02-10T12:28:24Z-
dc.date.available2020-02-10T12:28:24Z-
dc.identifier.issn2076-3417-
dc.identifier.urihttp://hdl.handle.net/10397/81647-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Han, S.; Li, H.; Li, M.; Rose, T. A Deep Learning Based Method for the Non-Destructive Measuring of Rock Strength through Hammering Sound. Appl. Sci. 2019, 9, 3484, 1-14 is available at https://dx.doi.org/10.3390/app9173484en_US
dc.subjectTransfer learningen_US
dc.subjectSpectrogram analysisen_US
dc.subjectSelecting samplesen_US
dc.subjectnon-destructive testingen_US
dc.subjectHammering sounden_US
dc.subjectRegression algorithmen_US
dc.titleA deep learning based method for the non-destructive measuring of rock strength through hammering sounden_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1-
dc.identifier.epage14-
dc.identifier.volume9-
dc.identifier.issue17-
dc.identifier.doi10.3390/app9173484-
dcterms.abstractHammering rocks of different strengths can make different sounds. Geological engineers often use this method to approximate the strengths of rocks in geology surveys. This method is quick and convenient but subjective. Inspired by this problem, we present a new, non-destructive method for measuring the surface strengths of rocks based on deep neural network (DNN) and spectrogram analysis. All the hammering sounds are transformed into spectrograms firstly, and a clustering algorithm is presented to filter out the outliers of the spectrograms automatically. One of the most advanced image classification DNN, the Inception-ResNet-v2, is then re-trained with the spectrograms. The results show that the training accurate is up to 94.5%. Following this, three regression algorithms, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) are adopted to fit the relationship between the outputs of the DNN and the strength values. The tests show that KNN has the highest fitting accuracy, and SVM has the strongest generalization ability. The strengths (represented by rebound values) of almost all the samples can be predicted within an error of [ 5, 5]. Overall, the proposed method has great potential in supporting the implementation of efficient rock strength measurement methods in the field.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationApplied sciences, 1 Sept. 2019, v. 9, no. 17, 3484, p. 1-14-
dcterms.isPartOfApplied sciences-
dcterms.issued2019-
dc.identifier.isiWOS:000488603600046-
dc.identifier.artn3484-
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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